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The Effect of the Normalization Strategy on Voxel-Based Analysis of DTI Images: A Pattern Recognition Based Assessment

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Brain Informatics (BI 2010)

Abstract

Quantitative analysis on diffusion tensor imaging (DTI) has shown be useful in the study of disease-related degeneration. More and more studies perform voxel-by-voxel comparisons of fractional anisotropy (FA) values, aiming at detecting white matter alterations. Overall, there is no agreement about how the normalization stage should be performed. The purpose of this study was to evaluate the effect of the normalization strategy on voxel-based analysis of DTI images, using the performance of a classification approach as objective measure of normalization quality. This is achieved by using a Support Vector Machine (SVM) which constructs a decision surface that allows binary classification with two types of regions, generated after a statistical evaluation of the grey level values of regions detected as statistically significant in a FA analysis.

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References

  1. Friston, K.J., Ashburner, J.T., Kiebel, S., Nichols, T.E., Penny, W.D. (eds.): Statistical Parametric Mapping: The analysis of functional brain images. Academic Press, London (2007)

    Google Scholar 

  2. Ashburner, J.: Computational anatomy with the spm software. Magnetic Resonance Imaging 27, 1163–1174 (2009)

    Article  Google Scholar 

  3. Kubicki, M., McCarley, R., Westin, C., Park, H., Maier, S., Kikinis, R., Shenton, M., Jolesz, F.: A review of diffusion tensor images un schizophrenia. Journal of psychiatric research 41, 15–30 (2007)

    Article  Google Scholar 

  4. Chao, T., Chou, M., Yang, P., Chung, H., Wu, M.T.: Effects of interpolation methods in spatial normalization of diffusion tensor imaging data on group comparison of fractional anisotropy. Magnetic Resonance Imaging 27, 681–690 (2008)

    Article  Google Scholar 

  5. Focke, N.: Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis. Neuroimage 40, 728–737 (2008)

    Article  Google Scholar 

  6. Kunimatsu, A.: Utilization of diffusion tensor tractography in combination with spatial normalization to asses involvement of the corticospinal tract in capsular/pericapsular stroke: feasibility and clinical implications. Magnetic Resonance Imaging 26, 1399–1404 (2007)

    Article  Google Scholar 

  7. Snook, L.: Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage 34, 243–252 (2007)

    Article  Google Scholar 

  8. Pell, G., Pardoe, H., Briellmann, R., Abbott, D., Jackson, G.: Sensitivity of voxel-based analysis of dti images to the warping strategy. In: Proceedings of the International Society for Magnetic Resonance in Medicine (2006)

    Google Scholar 

  9. Kloppel, S., Draganski, B.V., Golding, C., Chu, C., Nagy, Z., Cook, P.A., Hicks, S.L., Kennard, C., Alexander, D.C., Parker, G.J.M., Tabrizi, S.J., Frackowiak, R.S.J.: White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic huntington’s disease. Brain Advance (2007)

    Google Scholar 

  10. Caprihan, A., Pearlson, G., Calhoun, V.: Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements. Neuroimage 42, 675–682 (2008)

    Article  Google Scholar 

  11. Freidlin, R.Z., Ozarslan, E., Assaf, Y., Komlosh, M.E., Basser, P.J.: A multivariate hypothesis testing framework for tissue clustering and classification of dti data. NMR in Biomedicine 22, 716–729 (2009)

    Article  Google Scholar 

  12. Fan, Y., Shen, D.: Integrated feature extraction and selection for neuroimage classification. In: Proceedings of SPIE (2009)

    Google Scholar 

  13. Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr., C.R., Ashburner, J., Frackowiak, R.S.J.: Automatic classification of mr scans in alzheimer’s disease. Brain Advance Access (2008)

    Google Scholar 

  14. Plant, C., Teipel, S.J., Oswald, A., Bohm, C., Meindl, T., Mourao-Miranda, J., Bokde, A.W., Hampel, H., Ewers, M.: Automated detection of brain atrophy patterns based on mri for the prediction of alzheimer’s disease. NeuroImage (2009)

    Google Scholar 

  15. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning, pp. 185–208. MIT Press, Cambridge (1999)

    Google Scholar 

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Díaz, G. et al. (2010). The Effect of the Normalization Strategy on Voxel-Based Analysis of DTI Images: A Pattern Recognition Based Assessment. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science(), vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-15314-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15313-6

  • Online ISBN: 978-3-642-15314-3

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